Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation

arXiv:2403.12290v1 Announce Type: cross
Abstract: Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data. Slice propagation has emerged as an self-supervised approach that leverages slice registration as a self-supervised task to achieve full anatomy segmentation with minimal supervision. This approach significantly reduces the need for domain expertise, time, and the …

abstract analysis and analysis annotated data arxiv availability cs.cv data eess.iv performance propagation segmentation slice type uncertainty